Method And System For Visualizing Data From Electrical Source Imaging

Abstract

A method for visualizing data from electrical source imaging (ESI) is disclosed herein. The method converts the ESI into a plurality of ESI waveforms. The method generates a virtual electrode from the plurality of ESI waveforms. The method places the virtual electrode at a three-dimensional (3D) location of a representation of the patient's brain or on the surface of the scalp. The method receives a direct measurement of the virtual electrode at the 3D location.

Claims

1. A method for visualizing data from electrical source imaging (ESI), the method comprising: converting an ESI for a patient into a plurality of ESI waveforms, wherein the ESI is a combination of a model of a brain with a plurality of scalp signals from an EEG that estimates a source and intensity of a signal within the patent's brain; generating a virtual electrode from the plurality of ESI waveforms; placing the virtual electrode at a three-dimensional (3D) location of a representation of the patient's brain or on the surface of the scalp; and receiving a direct measurement of the virtual electrode at the 3D location.

2. The method according to claim 1 wherein the ESI comprises MRI imaging.

3. The method according to claim 1 wherein the ESI model of the patient's brain is created prior to the acquisition of an EEG.

4. The method according to claim 1 further comprising improving seizure and spike detection performance for an EEG.

5. The method according to claim 1 further comprising determining if there are more than one cluster of spikes for the patient.

6. A non-transitory computer-readable medium that stores a program that causes a processor to perform functions to visual data from electrical source imaging (ESI) by executing the following steps: converting an ESI for a patient into a plurality of ESI waveforms, wherein the ESI is a combination of a model of a brain with a plurality of scalp signals from an EEG that estimates a source and intensity of a signal within the patent's brain; generating a virtual electrode from the plurality of ESI waveforms; placing the virtual electrode at a three-dimensional (3D) location of a representation of the patient's brain; and receiving a direct measurement of the virtual electrode at the 3D location.

7. The non-transitory computer readable medium according to claim 6 wherein the ESI comprises MRI imaging.

8. The non-transitory computer readable medium according to claim 6 wherein the ESI model of the patient's brain is created prior to the generating an EEG.

9. The non-transitory computer readable medium according to claim 6 further comprising improving seizure and spike detection performance for an EEG.

10. The non-transitory computer readable medium according to claim 6 further comprising determining if there are more than one cluster of spikes for the patient.

11. A method for visualizing data from electrical source imaging (ESI) for stereo EEG (SEEG), the method comprising: converting a ESI for a patient into a plurality of ESI waveforms, wherein the ESI is a combination of a model of a brain with a plurality of scalp signals from an EEG that estimates a source and intensity of a signal within the patent's brain; generating a virtual electrode from the plurality of ESI waveforms; placing the virtual electrode at a three-dimensional (3D) location of a representation of the patient's brain; receiving a direct measurement of the virtual electrode at the 3D location; generating a virtual SEEG probe based on the measurement from the virtual electrode.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0055] FIG. 1 is a is a block diagram of a method for visualizing data from ESI.

[0056] FIG. 2 is an image of a quantitative EEG.

[0057] FIG. 3 is an illustration of a system for calculating a quantitative EEG with a patient having an open electrode.

[0058] FIG. 3A is an illustration of an isolated view of a patient with an open electrode.

[0059] FIG. 4 is a map for electrode placement for an EEG.

[0060] FIG. 5 is a detailed map for electrode placement for an EEG.

[0061] FIG. 6 is an illustration of a CZ reference montage.

[0062] FIG. 7 is an illustration of an EEG recording containing a seizure, a muscle artifact and an eye movement artifact.

[0063] FIG. 8 is an illustration of the EEG recording of FIG. 7 with the muscle artifact removed.

[0064] FIG. 9 is an illustration of the EEG recording of FIG. 8 with the eye movement artifact removed.

[0065] FIG. 10 is a flow chart for a method for visualizing data from ESI.

[0066] FIG. 11 is a flow chart for a method for visualizing data from ESI for SEEG.

[0067] FIG. 12 is an illustration of a system for calculating a quantitative EEG.

[0068] FIG. 13 is a block diagram of a system for calculating a quantitative EEG.

[0069] FIG. 14 is a flow chart for a method for visualizing data from ESI for SEEG.

[0070] FIG. 15 is a flow chart for a method for visualizing data from ESI.

DETAILED DESCRIPTION OF THE INVENTION

[0071] The present invention is a new method of visualizing the data from ESI. The invention provides a way to visualize data over longer time periods, and in a manner that is familiar to the key users electroencephalographers. (EEGers).

[0072] The primary diagnostic tool for epilepsy is the EEG. EEGers are trained over long periods of time to recognize the fundamental waveforms in an EEG recording and differentiate artifact from cerebral signal, and diagnostically relevant cerebral signals from the background. They can do this reliably and at high speed after years of medical training. In this invention we convert the results of ESI into waveforms so that the user can directly utilize these skills in interpretation. This will be particularly valuable in reviewing longer term events such as seizures.

[0073] At the core of the invention is the concept of a virtual electrode. A virtual electrode could be placed by a user anywhere in the brain with the ESI results measured in micro-volts (mV) presented as a time series in parallel to the actual scalp EEG. Micro-volts is chosen because EEG is represented in micro-volts and therefore the time series will look precisely like what an EEGer has been trained to view. But in this case instead of having to interpret the meaning of a set of scalp electrodes placed very far away from the relevant portion of the brain, the EERer will see an estimated direct measurement at a point of diagnostic interest. Users also can specify a multiplicity of virtual electrodes allowing for direct review at different points in the brain in parallel.

[0074] In epilepsy diagnostics, a patient's EEG is initially recorded non-invasively using scalp electrodes. Depending on the treatment path, patients eventually may be implanted with electrodes using a technique called Stereo EEG, or SEEG. In this technique a burr hole is drilled in the patients skull and a sensor “probe” is placed deeply into the patient's brain. On the probe, electrodes are spaced at known distances, typically from 2-10 mm. Several of these probes are generally implanted at once and the EEG is recorded for the patient over an extended time period. Generally the hope is that seizures will be captured and using these invasive electrodes, the seizure onset zone is more accurately identified.

[0075] An alternative embodiment is a virtual SEEG. In this embodiment, the user is provided with a way to simulate the implantation of one or more SEEG probes with the virtual electrode positions determined by the characteristics and placement of the probe. These virtual electrodes are added to the EEG display for the patient, thus simulating what would be seen in the case of an actual implantation. Depending on the scalp electrode count, there is less resolution than with the actual implanted electrodes, but the EEGer is able to use this to make predictions about what would be seen by any given choice of actual SEEG probe. Frequently the exact choice of position and quantity of probes is a difficult one to make. The desire is to implant the minimum necessary to locate the likely seizure onset zone.

[0076] The virtual electrode is a 3D coordinate location inside the patient's brain along with a circumference representing the area to be sampled. The idea is to have sets of these virtual electrodes constructed in arrays that match the types of implants used in intracranial EEG monitoring. These are termed grids and strips for subdural recording, and depth arrays used in stereo EEG recording. By placing these into an image of the patient's brain, a set of virtual electrode locations are established. The user could “implant” one or more virtual sets resulting in an array of electrode locations. This array would be displayed on an EEG page that looks like the page that is produced by actual invasive recordings.

[0077] FIG. 1 illustrates the method for visualizing data from ESI. An ESI 60 is generated for a patient 15 in step A. The ESI 60 is preferably a combination of a model of a brain with scalp signals from an EEG that estimates a source and intensity of a signal within the patient's brain. The ESI 60 is converted into ESI waveforms 61 in step B. In step C, a virtual electrode 75 is generated from the ESI waveforms 61, and placed at a 3D location of a representation of the patient's brain 76 or on the surface of the scalp. In step D, a direct measurement 77 of the virtual electrode 75 at the 3D location is received.

[0078] In addition to being able to display the simulated EEG at a virtual electrode 75 position it is possible to provide other features typically present in EEG systems such as the ability to re-montage, and to perform analytics such as Quantitative EEG (qEEG) 100, as shown in FIG. 2, an example of a QEEG 100.

[0079] In a system 20 for calculating a quantitative EEG, as shown in FIG. 3, a patient 15 wears an electrode cap 30, consisting of a plurality of electrodes 35a-35c, attached to the patient's head with wires 38 from the electrodes 35 connected to an EEG machine component 40 which consists of an amplifier 42 for amplifying the signal to a computer 41 with a processor, which is used to analyze the signals from the electrodes 35 and generate an EEG recording 51 and a qEEG, which can be viewed on a display 50. As shown in FIG. 3A, an electrode 850 is open, unattached, and over an impedance threshold value. Thus, if the signal from that electrode 850 is included in a qEEG, the qEEG value would be inaccurate. A more thorough description of an electrode utilized with the present invention is detailed in Wilson et al., U.S. Pat. No. 8,112,141 for a Method And Device For Quick Press On EEG Electrode, which is hereby incorporated by reference in its entirety. The EEG is optimized for automated artifact filtering. The EEG recordings are then processed using neural network algorithms to generate a processed EEG recording which is used to generate a qEEG.

[0080] An additional description of analyzing EEG recordings is set forth in Wilson et al., U.S. patent application Ser. No. 13/620855, filed on Sep. 15, 2012, for a Method And System For Analyzing An EEG Recording, which is hereby incorporated by reference in its entirety.

[0081] A patient has a plurality of electrodes attached to the patient's head with wires from the electrodes connected to an amplifier for amplifying the signal to a processor, which is used to analyze the signals from the electrodes and create an EEG recording. The brain produces different signals at different points on a patient's head. Multiple electrodes are positioned on a patient's head as shown in FIGS. 4 and 5. The CZ site is in the center. For example, Fp1 on FIG. 5 is represented in channel FP1-F3 on FIG. 7. The number of electrodes determines the number of channels for an EEG. A greater number of channels produce a more detailed representation of a patient's brain activity. If an electrode is open, then the recording for the channel is inaccurate thereby generating false readings. Preferably, each amplifier 42 of an EEG machine component 40 corresponds to two electrodes 35 attached to a head of the patient 15. The output from an EEG machine component 40 is the difference in electrical activity detected by the two electrodes. The placement of each electrode is critical for an EEG report since the closer the electrode pairs are to each other, the less difference in the brainwaves that are recorded by the EEG machine component 40. A more thorough description of an electrode utilized with the present invention is detailed in Wilson et al., U.S. Pat. No. 8,112,141 for a Method And Device For Quick Press On EEG Electrode, which is hereby incorporated by reference in its entirety.

[0082] The EEG is optimized for automated artifact filtering. The EEG recordings are then processed using neural network algorithms to generate a processed EEG recording, which is analyzed for display. During acquisition of the EEG recording, a processing engine performs continuous analysis of the EEG waveforms and determines the presence of most types of electrode artifact on a channel-by-channel basis. Much like a human reader, the processing engine detects artifacts by analyzing multiple features of the EEG traces. The preferred artifact detection is independent of impedance checking. During acquisition the processing monitors the incoming channels looking for electrode artifacts. When artifacts are detected they are automatically removed from the seizure detection process and optionally removed from the trending display. This results in much a much higher level of seizure detection accuracy and easier to read trends than in previous generation products.

[0083] Algorithms for removing artifact from EEG typically use Blind Source Separation (BSS) algorithms like CCA (canonical correlation analysis) and ICA (Independent Component Analysis) to transform the signals from a set of channels into a set of component waves or “sources.”

[0084] In one example an algorithm called BSS-CCA is used to remove the effects of muscle activity from the EEG. Using the algorithm on the recorded montage will frequently not produce optimal results. In this case it is generally optimal to use a montage where the reference electrode is one of the vertex electrodes such as CZ in the international 10-20 standard. In this algorithm the recorded montage would first be transformed into a CZ reference montage prior to artifact removal. In the event that the signal at CZ indicates that it is not the best choice then the algorithm would go down a list of possible reference electrodes in order to find one that is suitable.

[0085] It is possible to perform BSS-CCA directly on the user-selected montage. However, this has two issues. First this requires doing an expensive artifact removal process on each montage selected for viewing by the user. Second the artifact removal will vary from one montage to another, and will only be optimal when a user selects a referential montage using the optimal reference. Since a montage that is required for reviewing an EEG is frequently not the same as the one that is optimal for removing artifact this is not a good solution.

[0086] Various trends for an EEG recording are generated by a processing engine. A seizure probability trend, a rhythmicity spectrogram, left hemisphere trend, a rhythmicity spectrogram, right hemisphere trend, a FFT spectrogram left hemisphere trend, a FFT spectrogram right hemisphere trend, an asymmetry relative spectrogram trend, an asymmetry absolute index trend, an aEEG trend, and a suppression ration, left hemisphere and right hemisphere trend.

[0087] Rhythmicity spectrograms allow one to see the evolution of seizures in a single image. The rhythmicity spectrogram measures the amount of rhythmicity which is present at each frequency in an EEG record.

[0088] The seizure probability trend shows a calculated probability of seizure activity over time. The seizure probability trend shows the duration of detected seizures, and also suggests areas of the record that may fall below the seizure detection cutoff, but are still of interest for review. The seizure probability trend when displayed along with other trends, provides a comprehensive view of quantitative changes in an EEG.

[0089] A method for visualizing data from ESI is generally designated 600 in FIG. 10. At block 601, an ESI for a patient is generated. The ESI is a combination of a model of a brain with a plurality of scalp signals from an EEG that estimates a source and intensity of a signal within the patient's brain. At block 602, the ESI is converted into a plurality of ESI waveforms. At block 603, a virtual electrode is generated from the plurality of ESI waveforms. At block 604, the virtual electrode is placed at a 3D location of a representation of the patient's brain or on the surface of the scalp. At block 605, a direct measurement of the virtual electrode at the 3D location is received.

[0090] The ESI of the method 600 preferably comprises MRI imaging. The ESI model of the patient's brain is preferably created prior to the acquisition of an EEG.

[0091] The method 600 further comprises improving seizure and spike detection performance for an EEG, and determining if there are more than one cluster of spikes for the patient.

[0092] EEG signals are generated from an EEG machine comprising a plurality of electrodes, an amplifier and processor. The EEG signals are processed continuously for artifact reduction to generate a processed EEG recording. A quantitative EEG is calculated from the processed EEG recording. Preferably, Fast Fourier Transform signal processing is used to compute the quantitative EEG. The reduced artifact types are selected from the group comprising an eye blink artifact, a muscle artifact, a tongue movement artifact, a chewing artifact, and a heartbeat artifact.

[0093] As shown in FIG. 11, a method for visualizing data from ESI for stereo EEG (SEEG) is generally designated 700. At block 701, an ESI for a patient is generated, wherein the ESI is a combination of a model of a brain with a plurality of scalp signals from an EEG that estimates a source and intensity of a signal within the patient's brain. At block 702, the ESI is converted into a plurality of ESI waveforms. A virtual electrode is generated from the plurality of ESI waveforms at block 703. At block 704, the virtual electrode is placed at a 3D location of a representation of the patient's brain. At block 705, a direct measurement of the virtual electrode at the 3D location is received. At block 706, a virtual SEEG probe based on the measurement from the virtual electrode is generated.

[0094] In a system for calculating a quantitative EEG, as shown in FIG. 12, a patient 15 wears an electrode cap 30, consisting of a plurality of electrodes 35a-35c, attached to the patient's head with wires 38 from the electrodes 35 connected to an EEG machine component 40 which consists of an amplifier 42 for amplifying the signal to a computer 41 with a processor, which is used to analyze the signals from the electrodes 35 and generate an EEG recording and a qEEG 51, which can be viewed on a display 50. The CPU 41 includes a software program for a neural network algorithm and a software program for a qEEG engine. As shown in FIG. 13, an artifact reduction engine 46, a qEEG engine 47, a microprocessor 44, a memory 42, a memory controller 43 and an I/O 48 are components of the EEEG machine 40. A more thorough description of an electrode utilized with the present invention is detailed in Wilson et al., U.S. Pat. No. 8,112,141 for a Method And Device For Quick Press On EEG Electrode, which is hereby incorporated by reference in its entirety. The EEG is optimized for automated artifact filtering. The EEG recordings are then processed using neural network algorithms to generate a processed EEG recording which is analyzed for display.

[0095] A method for visualizing data from ESI for SEEG is generally designated 800 in FIG. 14. At block 801, an ESI for a patient is generated. At block 802, the ESI is converted into a plurality of ESI waveforms. A plurality of virtual electrodes is generated from the plurality of ESI waveforms at block 803. At block 804, each of the plurality of virtual electrodes is placed at a 3D location of a representation of the patient's brain. At block 805, a direct measurement from each of the virtual electrodes at the 3D location is received. At block 806, a virtual SEEG probe based on the measurement from each of the virtual electrodes is generated.

[0096] A method for visualizing data from ESI is generally designated 900 in FIG. 15. At block 901, an ESI for a patient is generated. At block 902, the ESI is converted into a plurality of ESI waveforms. A plurality of virtual electrodes is generated from the plurality of ESI waveforms at block 903. At block 904, each of the plurality of virtual electrodes is placed at a 3D location of a representation of the patient's brain. At block 905, a direct measurement from each of the virtual electrodes at the 3D location is received.

[0097] A more thorough description of EEG analysis utilized with the present invention is detailed in Wilson et al., U.S. patent application Ser. No. 13/620855, filed on Sep. 15, 2012, for a Method And System For Analyzing An EEG Recording, which is hereby incorporated by reference in its entirety. A more thorough description of a user interface utilized with the present invention is detailed in Wilson et al., U.S. Pat. No. 9,055,927, for a User Interface For Artifact Removal In An EEG, which is hereby incorporated by reference in its entirety. An additional description of analyzing EEG recordings is set forth in Wilson et al., U.S. patent application Ser. No. 13/684556, filed on Nov. 25, 2012, for a Method And System For Detecting And Removing EEG Artifacts, which is hereby incorporated by reference in its entirety. A more thorough description of displaying an EEG utilized with the present invention is detailed in Nierenberg et al., U.S. Pat. No. 8,666,484, for a Method And System For Displaying EEG Recordings, which is hereby incorporated by reference in its entirety. A more thorough description of displaying EEG recordings utilized with the present invention is detailed in Wilson et al., U.S. Pat. No. 9,232,922, for a User Interface For Artifact Removal In An EEG, which is hereby incorporated by reference in its entirety. An additional description of qEEG is set forth in Nierenberg et al., U.S. patent application Ser. No. 13/830742, filed on Mar. 14, 2013, for a Method And System To Calculate qEEG, which is hereby incorporated by reference in its entirety. An additional description of using neural networks with the present invention is set forth in Wilson, U.S. patent application Ser. No. 14/078497, filed on Nov. 12, 2013, for a Method And System Training A Neural Network, which is hereby incorporated by reference in its entirety. An additional description of using neural networks with the present invention is set forth in Nierenberg et al., U.S. patent application Ser. No. 14/222655, filed on Jan. 20, 2014, for a System And Method For Generating A Probability Value For An Event, which is hereby incorporated by reference in its entirety. Wilson et al., U.S. patent application Ser. No. 16/294917, filed on Mar. 7, 2019, for a Method And System For Utilizing Empirical Null Hypothesis For a Biological Time Series, which is hereby incorporated by reference in its entirety. Wilson et al., U.S. patent application Ser. No. 16/288731, filed on Feb. 28, 2019, for a Graphically Displaying Evoked Potentials, which is hereby incorporated by reference in its entirety.

[0098] From the foregoing it is believed that those skilled in the pertinent art will recognize the meritorious advancement of this invention and will readily understand that while the present invention has been described in association with a preferred embodiment thereof, and other embodiments illustrated in the accompanying drawings, numerous changes modification and substitutions of equivalents may be made therein without departing from the spirit and scope of this invention which is intended to be unlimited by the foregoing except as may appear in the following appended claim. Therefore, the embodiments of the invention in which an exclusive property or privilege is claimed are defined in the following appended claims.